
"""simple docstring"""






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 			lowercase					( lowercase__		):
			lowercase        =     (KDPMaDiscreteScheduler,)
			lowercase        =     10






			def 					UpperCAmelCase							(self  :	Dict      ,**SCREAMING_SNAKE_CASE_  :	Dict			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  {
				    '''num_train_timesteps''': 1_100,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				}

				config.update(**SCREAMING_SNAKE_CASE_			)
				return config






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	Tuple:





				"""simple docstring"""


				for timesteps in [10, 50, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	Tuple:





				"""simple docstring"""


				for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]      ,[0.00_02, 0.0_02, 0.02]			):
					self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_      ,beta_end=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[Any]:





				"""simple docstring"""


				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.scheduler_classes[0]
				lowerCAmelCase												=  self.get_scheduler_config(prediction_type='''v_prediction'''			)
				lowerCAmelCase												=  scheduler_class(**SCREAMING_SNAKE_CASE_			)

				scheduler.set_timesteps(self.num_inference_steps			)

				lowerCAmelCase												=  self.dummy_model()
				lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase												=  sample.to(SCREAMING_SNAKE_CASE_			)

				for i, t in enumerate(scheduler.timesteps			):
					lowerCAmelCase												=  scheduler.scale_model_input(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  output.prev_sample

				lowerCAmelCase												=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
				lowerCAmelCase												=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

				if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 4.6_934e-07			) < 1e-2
					assert abs(result_mean.item() - 6.1_112e-10			) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 4.693_428_650_170_972e-07			) < 1e-2
					assert abs(result_mean.item() - 0.00_02			) < 1e-3






			def 					UpperCAmelCase							(self  :	int			)       ->	Union[str, Any]:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase												=  self.scheduler_classes[0]
				lowerCAmelCase												=  self.get_scheduler_config()
				lowerCAmelCase												=  scheduler_class(**SCREAMING_SNAKE_CASE_			)

				scheduler.set_timesteps(self.num_inference_steps			)

				lowerCAmelCase												=  self.dummy_model()
				lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase												=  sample.to(SCREAMING_SNAKE_CASE_			)

				for i, t in enumerate(scheduler.timesteps			):
					lowerCAmelCase												=  scheduler.scale_model_input(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  output.prev_sample

				lowerCAmelCase												=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
				lowerCAmelCase												=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

				if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 20.41_25			) < 1e-2
					assert abs(result_mean.item() - 0.02_66			) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 20.41_25			) < 1e-2
					assert abs(result_mean.item() - 0.02_66			) < 1e-3






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Optional[int]:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase												=  self.scheduler_classes[0]
				lowerCAmelCase												=  self.get_scheduler_config()
				lowerCAmelCase												=  scheduler_class(**SCREAMING_SNAKE_CASE_			)

				scheduler.set_timesteps(self.num_inference_steps      ,device=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self.dummy_model()
				lowerCAmelCase												=  self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					lowerCAmelCase												=  scheduler.scale_model_input(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  output.prev_sample

				lowerCAmelCase												=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
				lowerCAmelCase												=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

				if str(SCREAMING_SNAKE_CASE_			).startswith('''cpu'''			):
					# The following sum varies between 148 and 156 on mps. Why?
					assert abs(result_sum.item() - 20.41_25			) < 1e-2
					assert abs(result_mean.item() - 0.02_66			) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 20.41_25			) < 1e-2
					assert abs(result_mean.item() - 0.02_66			) < 1e-3




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	str      ,*SCREAMING_SNAKE_CASE_  :	Any      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	None:





				"""simple docstring"""


				warnings.warn(
				    '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use PerceiverImageProcessor instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
				super().__init__(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)


